Made-With-ML VS awesome-mlops

Made With ML

- Develop a (simple!) ML project and apply MLOps best practices to it. Ask Chat GPT all of your MLOps questions. I've joined this MLOps community and it has been very helpful to know what path to follow in order to be better at MLOps, thanks to them I arrived at madewithml, but I haven't done it yet. But it covers all the MLOps side.

Hey, I’m also working in ML. Here’s a great resource: https://madewithml.com. Also, check out Noah Gift’s book Practical MLOPs.

Try replicating this setup: https://madewithml.com/

madewithml

There's a really nice website by Goku Mohandas called Made With ML. IMO it is the best practical guide to MLOps out there: https://madewithml.com

Incase you want to dive a little deeper, https://fullstackdeeplearning.com/course/2022/ is also something I have been recommended by folks.

madewithml is pretty sweet, especially the MLOps side of things. It'll give you good skills in how development in Python and deploying ML works.

I'm in the same boat. Data scientist shifting towards ML engineering-MLOps. The guide seems quite complete. I am also doing the ML DevOps engineer, which has end-to-end projects and has been helpful so far. I also feel that most ML engineers will be Mlops too, as most companies will not distinguish between the two, so I try to focus on this part. Here is a quite comprehensive list of resources: https://github.com/visenger/awesome-mlops

Good Reference: https://github.com/visenger/awesome-mlops (The Link above has so many Guides, It's insane) https://madewithml.com/

I recently did a deep-dive into MLOps for a client, and I've found that https://ml-ops.org/ offers a great overview. Some topics are a bit too "zoomed out", but they still touch on most important aspects of building an end-to-end product. I found it a great starting point when doing research, and picking and choosing some key points from each section + some google helped a lot. Give it a look, you'll probably find some useful things in there.

MLOps

She made mentions to ML-Ops and MLFlow including Vertex AI the GCP implementation. I will post the video as soon as it is available. In the meantime, you can enjoy any other talk from Nerea Luis

From your post, you seem to be trained for data science for physics modeling, so I'd recommend to get started with https://ml-ops.org/ and for the data engineering part, I found this https://github.com/andkret/Cookbook open source cookbook to be invaluable.